doddle-model

What it is

doddle-model is an in-memory machine learning library that provides immutable objects and exposes its functionality through a scikit-learn-like API, making it easy for developers familiar with Python's scikit-learn to transition to Scala-based ML projects.

doddle-model stands out by offering an in-memory machine learning experience with immutable objects and a scikit-learn-like API, making it ideal for Scala developers familiar with Python's ML ecosystem.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Steep learning curve for non-Scala developers

   The library is built in Scala, which may be unfamiliar to many data scientists and machine learning practitioners who are more accustomed to Python.

2. 02

   Limited community support and documentation

   As an open-source project with a smaller user base compared to popular Python libraries like scikit-learn, doddle-model may have less comprehensive documentation and fewer community resources available for troubleshooting.

3. 03

   Performance limitations due to in-memory processing

   In-memory processing can be a bottleneck when dealing with large datasets that do not fit into RAM, limiting the scalability of doddle-model for big data applications.

4. 04

   Frequent breaking changes between versions

   The library has undergone significant API changes in recent updates, which can lead to maintenance overhead and instability when upgrading versions.

What it does well

• In-memory machine learning library built on top of Breeze

• Immutable objects for better functional programming practices

• Scikit-learn-like API for ease of use and familiarity

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.